5 papers
Constrained Adversarial Learning for Automated Software Testing: a literature review
João Vitorino, Tiago Dias, Tiago Fonseca +2
It is imperative to safeguard computer applications and information systems against the growing number of cyber-attacks. Automated software testing tools can be developed to quickl…
Evaluating LLaMA 3.2 for Software Vulnerability Detection
José Gonçalves, Miguel Silva, Bernardo Cabral +5
Deep Learning (DL) has emerged as a powerful tool for vulnerability detection, often outperforming traditional solutions. However, developing effective DL models requires large amo…
Network Simulation with Complex Cyber-attack Scenarios
Tiago Dias, João Vitorino, Eva Maia +1
Network Intrusion Detection (NID) systems can benefit from Machine Learning (ML) models to detect complex cyber-attacks. However, to train them with a great amount of high-quality…
SCoPE: Evaluating LLMs for Software Vulnerability Detection
José Gonçalves, Tiago Dias, Eva Maia +1
In recent years, code security has become increasingly important, especially with the rise of interconnected technologies. Detecting vulnerabilities early in the software developme…
FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer
Tiago Dias, Eva Maia, Isabel Praça
Software's pervasive impact and increasing reliance in the era of digital transformation raise concerns about vulnerabilities, emphasizing the need for software security. Fuzzy tes…